The processing considered is that of the non-supervised deinterleaving of the pulse trains emitted by electromagnetic emitters. With reference to FIG. 1, when an ESM or ELINT sensor, intercepts these pulse trains, it processes all the pulses whose power is greater than its sensitivity threshold. The emissions being present simultaneously, these pulse trains are nested or “interleaved”.
These situations of mixing or interleaving of pulse trains are all the more frequent the more the sensitivity of the ESM sensors increases, bringing about an increase in the density of the detected pulses, and the more the agile emissions spread their pulses over the spectrum, thereby increasing the probability of finding pulses of several radars at a given frequency.
To process these situations the extractor must first of all isolate each emission of interest so as thereafter to be able to analyze it and characterize it. One speaks of “Deinterleaving”.
The intercepted radar emissions are broadly of three types:                TE1: This corresponds to emissions of High and Medium Recurrence frequency (HRF/MRF) exhibiting volumes of 30000 to more than 100000 pulses per second per emission. These emissions are of simple characteristics, that is to say of Pulse Repetition Interval (PRI) and/or stable Frequency per train. They are generally processed by fast and robust ad hoc schemes.        TE2: This class corresponds to the Low Recurrence Frequency (LRF) emissions exhibiting volumes of 1000 pulses per second per emission. These emissions can exhibit complex characteristics such as for example a variable repetition period (or “stagger” as it is also known) of high order or pulse to pulse frequency agility.        TE3: This type of emission refers to the LRF emissions with pulse to pulse agility of PRI and of frequency. Or emissions with Low Probability of Intercept (LPI) with low number of pulses and heavily modulated. Diverse processing schemes are usable such as for example, recognition on the intrapulse parameters and DOA or sorting by Direction Of Arrival (DOA) and location.        
Subsequently, we will be concerned with the deinterleaving of emissions of type TE2 and TE3. Emissions of type TE1 are processed separately by fast algorithms.
Two complementary deinterleaving functions are distinguished:                Supervised Deinterleaving (SD), in which the known characteristics of the signals such as for example, the Frequencies (F), the values of PRI, the Pulse Durations (PD) or the IntraPulse (IP) parameters, are used to recognize the pulses originating from these signals.        Non Supervised Deinterleaving (NSD), in which one has no knowledge of these characteristics.        
In most ESM or ELINT extractors these two functions cooperate as illustrated in FIG. 2.
The NSD schemes can be segmented between:                Those which use classifications (or clustering) on primary parameters of the pulse (DOA, F, PD, IP)        Those which use classifications on the basis of the Times of Arrival of the pulses (TOA) corresponding to the secondary parameter PRI.        
In most extractors the two schemes are used in succession such as for example according to the diagram of FIG. 3.
Moreover in these extractors the deinterleaving and the tracking are carried out gradually while not examining the entirety of the information simultaneously.
A large volume of data requires to be processed by an ESM or ELINT sensor with fast reaction times. In general the computational power required evolves more rapidly than the evolution in the power of processors (Moore's law) on account of the increase in the amount of data to be processed and of the hitherto nonlinear complexity of the processing. In a nonlimiting manner, the following reasons may explain the drastic increase in data volumes:                ELINT sensors have ever higher sensitivities for the detection of certain radars.        
Emissions are agile, and thus, for maximum sensing of the waveform, it is necessary:                to have sizable reception bands,        to have long listening times.        
The increase in the temporal density of the pulses also introduces effects of superposition of simultaneous pulses implying the loss of part of the pulses of a waveform.
The channelisation of certain receivers makes it necessary to scan the useful reception band, allowing only partial acquisition of the waveform.
It is appreciated that if these waveforms are observed over long times, it will be possible to recover statistical indices and to relate them.
To summarize, it is noted that:                data volumes are increasing;        it would be useful to have analyzes over long listening times;        it is necessary to process the parameters in a conjoint analyzis.        
Current algorithms do not make it possible to fulfill these various constraints.
If the complexity of the algorithm is merely quadratic this signifies in practice that processing 1 second of listening duration costs 100 times as much, in computation time, as processing 100 ms of listening duration. This becomes 10000 for 10 seconds. Current algorithms often have complexities that are more than quadratic. This prohibits the application of these algorithms over sizable durations although this is interesting for revealing statistical discriminants. We give two examples of non-trivial long-time statistical indices: certain frequency values (ditto PRIs) are often associated over one and the same DOA in one and the same listening; certain radars scan space regularly and exhibit an Antenna Rotation Period or ARP. These indices can be used for long time whereas they cannot be used for short time.
In this context, a need exists for efficient implementations which minimize computational complexity. The optimum of this complexity is linear complexity that is to say the fact that the number of computations increases linearly with the rate of the incoming data. With linear complexity it becomes possible to analyze very sizable volumes of data, therefore to work over long times with a high pulse density.
The principles conventionally used in NSD is generally based on separation by beam intercept times, separation by Direction of Arrival, separation by primary parameters (Fr, PW, IP) or separation by Histogram of Difference Time Of Arrival (HDTOA).
Separation by beam intercept times is based on the statistical decorrelation of the dates of beams for an environment of radars. For a mean density of radars (and having undertaken a first coarse filtering on the DOA or/and the frequency) the illuminations of the various radars appear rarely “interleaved”.
This technique is however rather more suitable for warning detectors (or RWR for Radar Warning Receiver) which fulfill their warning role with a sensitivity that does not in general allow them to intercept the radars on scattered lobes. This technique is obviously not suitable for a receiving system which must intercept the radars on scattered lobes (ELINT) or in a dense environment since in this case the system always sees the emissions “interleaved”.
A reliable parameter which is easy to use to deinterleave the pulses is the direction of arrival (DOA). It is manifestly obvious that this is the only parameter that a modern radar cannot modulate; this is why the DOA when it is measured has an essential role in the extraction method. But in numerous systems the DOA exhibits a “low” resolution quality of the order of 10°. Moreover, attention must be paid when the DOA measurement is defective (measurement on the cross-polarization of the reception aerials) or when several emissions are observed in the same angular sector.
Another scheme consists in using the primary parameters to separate the pulses. In addition to the DOA parameter, most “conventional” deinterleaving algorithms are based on the use of simple sortings on single-pulse parameters, namely essentially pulse frequency and duration. These extractors based on sortings on single-pulse parameters have been used for many years and can still be used in certain simple situations. The existence will be noted of more elaborate sorting algorithms than in-line simple sortings (pulse by pulse) based on a statistical approach (for example splitting of the modes of an histogram) but which are further reduced to the use of the primary parameters.
The generalization of the pulse to pulse agility and of the agility based on trains of the waveforms makes today difficult or impossible this solution. Indeed in a dense environment it appears impossible in certain angular sectors (even with a sensor with high resolution) to separate two unknown agile emissions by simple frequency sorting. Either the values of brackets are wide and a mixture of the two emissions is obtained. Or else the values of brackets are narrow and each emission is split into a myriad of monofrequency clusters of pulses. To solve the agility two techniques have appeared conjointly:                on the one hand the use of the DTOA (for Difference of Time Of Arrival).        on the other hand the use of schemes for recombining the groups of pulses arising from monofrequency sortings (+DOA) with a view to solving the problem due to the agilities.        
The introduction of the difference of time of arrival (or DTOA) makes it possible in the case of a pulse train with fixed PRI to track the corresponding pulses; then looking at pulses separated by “equal time intervals” is enough.
The above two steps are found again:                A step of detecting the grouping of pulses (here a grouping around an RPI) carried out by virtue of a histogram of the DTOAs or a Fourier Transform (FT) of the TOAs.        A step of selecting the pulses corresponding to an PRI, which we will call “gating”.        
It is possible to use several types of transformation of the DTOAs improperly all called DTOA histograms (or HDTOA), to cite only the most noteworthy:                The k-order HDTOA and its possible variants (complete Histogram, Alldiff progressive Histogram, sequential progressive Histogram) which is a simple summation of the numbers of occurrences that a time disparity is observed. This histogram is the one usually cited or used. It is equivalent to the autocorrelation function of the sequence of the TOAs. Its defect is to reveal all the spectral lines of PRIs as well as their linear combinations.        The so-called “compressed” HDTOA which sums a complex argument used to take into account the periodic character of the DTOAs: Extraction of the Pattern Repetition Period (or PRP). It exhibits defects when pulses are missing, sometimes bringing about losses of the PRP in case of variable PRI (or “staggers” as they are also known).        The two-dimensional HDTOA which extends the search for the PRIs to the search for significant PRI pairs (or transition). It exhibits the benefit of having a reduced level of interactions compared to the previous ones. Its defect is the computational cost.        The histogram of the doublets which is very efficient to search for the PRPs. It is shown that it corresponds to a partial construction of the two-dimensional histogram and that it exhibits the same level of interactions. The benefit thereof is to reduce the information contained in the TOAs to just the PRP spectral lines.        The Fourier Histogram. The techniques based on the construction of histograms of DTOA are not robust to jittered PRIs, especially when there is a mixture. The computation of the Fourier transform of a comb of N Diracs centered at the instants of arrival TOA of the pulses exhibits a maximum at the frequency PRF corresponding to the inverse of the PRI of the pulses. The computation of the FT at all the possible frequencies is however very time-expensive. Various algorithms of this type have been described in the literature.        
These histograms have various properties. All these histograms can have high robustness to missing pulses.
The DTOA based gating having isolated an PRI on HDTOA or a PRF on periodogram, a phase of searching for the corresponding pulses is used. Accordingly the pulse trains corresponding to each detected PRP are extracted in several successive sequences. The result is the obtaining of series of pulses at this PRP.
The choice among the variants presented is tightly related to the severity of the environments, to the quality of the primary measurements, and to the power of computation possible with the CPU resource allocated for the application.
The DTOA-based extraction can be extended to any “contrasted” temporal structure of the waveform. It is noted moreover that for the agile and non-agile waveforms the temporal structure (the PRIs) is more discriminating, more characteristic and more stable than the frequency structure. Indeed it is simpler to alter the emission frequencies than the sets of PRIs used.
The deinterleaving techniques based on the DTOA also have their limits and thus their efficiency decreases with the arrival of the WaveForms with Agility of PRI, agility in the sense of an unstable temporal structure. Indeed a variable repetition period (or “Stagger”) of PRI is a so-called PRI-agile emission but remains very easy to process by a deinterleaving algorithm based on the DTOA since the pattern is stable over time. Conversely electronic scanning radars can emit a signal which seen from a point in space does not exhibit any apparent temporal structure. In this case the deinterleaving may not be based on the DTOA.
Although the electromagnetic environment is composed of a large quantity of stable PRI pattern waveforms, it will be very useful to take the conventional sorting parameters into account conjointly, in order to solve the case of the rather few signals with unstable PRI. Furthermore the DTOA based techniques can be damaged by the fortuitous synchronism of emissions with identical PRIs and perceived simultaneously by the sensor. In this case the separation information must be afforded by other parameters (DOA, Level, Frequency, PW, IP . . . ).